Defining window-boundaries for genomic analyses using smoothing spline techniques

Background

High-density genomic data is often analyzed by combining information over windows of adjacent markers. Interpretation of data grouped in windows versus at individual locations may increase statistical power, simplify computation, reduce sampling noise, and reduce the total number of tests performed. However, use of adjacent marker information can result in over- or under-smoothing, undesirable window boundary specifications, or highly correlated test statistics. We introduce a method for defining windows based on statistically guided breakpoints in the data, as a foundation for the analysis of multiple adjacent data points. This method involves first fitting a cubic smoothing spline to the data and then identifying the inflection points of the fitted spline, which serve as the boundaries of adjacent windows. This technique does not require prior knowledge of linkage disequilibrium, and therefore can be applied to data collected from individual or pooled sequencing experiments. Moreover, in contrast to existing methods, an arbitrary choice of window size is not necessary, since these are determined empirically and allowed to vary along the genome.

Results

Simulations applying this method were performed to identify selection signatures from pooled sequencing F_ST data, for which allele frequencies were estimated from a pool of individuals. The relative ratio of true to false positives was twice that generated by existing techniques. A comparison of the approach to a previous study that involved pooled sequencing F_ST data from maize suggested that outlying windows were more clearly separated from their neighbors than when using a standard sliding window approach.

Conclusions

We have developed a novel technique to identify window boundaries for subsequent analysis protocols. When applied to selection studies based on F_ST data, this method provides a high discovery rate and minimizes false positives. The method is implemented in the R package GenWin, which is publicly available from CRAN.     

Deficiency in the mitochondrial apoptotic pathway reveals the toxic potential of autophagy under ER stress conditions

Endoplasmic reticulum (ER) stress-induced cell death is normally associated with activation of the mitochondrial apoptotic pathway, which is characterized by CYCS (cytochrome c, somatic) release, apoptosome formation, and caspase activation, resulting in cell death. In this study, we demonstrate that under conditions of ER stress cells devoid of CASP9/caspase-9 or BAX and BAK1, and therefore defective in the mitochondrial apoptotic pathway, still undergo a delayed form of cell death associated with the activation of caspases, therefore revealing the existence of an alternative stress-induced caspase activation pathway. We identified CASP8/caspase-8 as the apical protease in this caspase cascade, and found that knockdown of either of the key autophagic genes, ATG5 or ATG7, impacted on CASP8 activation and cell death induction, highlighting the crucial role of autophagy in the activation of this novel ER stress-induced death pathway. In line with this, we identified a protein complex composed of ATG5, FADD, and pro-CASP8 whose assembly coincides with caspase activation and cell death induction. Together, our results reveal the toxic potential of autophagy in cells undergoing ER stress that are defective in the mitochondrial apoptotic pathway, and suggest a model in which the autophagosome functions as a platform facilitating pro-CASP8 activation. Chemoresistance, a common problem in the treatment of cancer, is frequently caused by the downregulation of key mitochondrial death effector proteins. Alternate stress-induced apoptotic pathways, such as the one described here, may become of particular relevance for tackling the problem of chemoresistance in cancer cells.     

An improved formulation of the zirconyl hematoxylin stain for acidic mucins

Zirconyl hematoxylin stains acidic mucins darkly and specifically using a solution of 100 mg hematoxylin, 5 ml ethanol, 5 ml 0.5% sodium iodate, 400 mg zirconyl chloride octahydrate, and 30 ml 25% aqueous glycerol. The stain is especially advantageous for studying goblet cells and Paget cells.     

Low-dose CT measurements of airway dimensions and emphysema associated with airflow limitation in heavy smokers: a cross sectional study

Background

Increased airway wall thickness (AWT) and parenchymal lung destruction both contribute to airflow limitation. Advances in computed tomography (CT) post-processing imaging allow to quantify these features. The aim of this Dutch population study is to assess the relationships between AWT, lung function, emphysema and respiratory symptoms.

Methods

AWT and emphysema were assessed by low-dose CT in 500 male heavy smokers, randomly selected from a lung cancer screening population. AWT was measured in each lung lobe in cross-sectionally reformatted images with an automated imaging program at locations with an internal diameter of 3.5 mm, and validated in smaller cohorts of patients. The 15^th percentile method (Perc15) was used to assess the severity of emphysema. Information about respiratory symptoms and smoking behavior was collected by questionnaires and lung function by spirometry.

Results

Median AWT in airways with an internal diameter of 3.5 mm (AWT_3.5) was 0.57 (0.44 - 0.74) mm. Median AWT in subjects without symptoms was 0.52 (0.41-0.66) and in those with dyspnea and/or wheezing 0.65 (0.52-0.81) mm (p<0.001). In the multivariate analysis only AWT_3.5 and emphysema independently explained 31.1%and 9.5%of the variance in FEV₁%predicted, respectively, after adjustment for smoking behavior.

Conclusions

Post processing standardization of airway wall measurements provides a reliable and useful method to assess airway wall thickness. Increased airway wall thickness contributes more to airflow limitation than emphysema in a smoking male population even after adjustment for smoking behavior.     

Prolonged cigarette smoke exposure alters mitochondrial structure and function in airway epithelial cells

Background

Cigarette smoking is the major risk factor for COPD, leading to chronic airway inflammation. We hypothesized that cigarette smoke induces structural and functional changes of airway epithelial mitochondria, with important implications for lung inflammation and COPD pathogenesis.

Methods

We studied changes in mitochondrial morphology and in expression of markers for mitochondrial capacity, damage/biogenesis and fission/fusion in the human bronchial epithelial cell line BEAS-2B upon 6-months from ex-smoking COPD GOLD stage IV patients to age-matched smoking and never-smoking controls.

Results

We observed that long-term CSE exposure induces robust changes in mitochondrial structure, including fragmentation, branching and quantity of cristae. The majority of these changes were persistent upon CSE depletion. Furthermore, long-term CSE exposure significantly increased the expression of specific fission/fusion markers (Fis1, Mfn1, Mfn2, Drp1 and Opa1), oxidative phosphorylation (OXPHOS) proteins (Complex II, III and V), and oxidative stress (Mn-SOD) markers. These changes were accompanied by increased levels of the pro-inflammatory mediators IL-6, IL-8, and IL-1β. Importantly, COPD primary bronchial epithelial cells (PBECs) displayed similar changes in mitochondrial morphology as observed in long-term CSE-exposure BEAS-2B cells. Moreover, expression of specific OXPHOS proteins was higher in PBECs from COPD patients than control smokers, as was the expression of mitochondrial stress marker PINK1.

Conclusion

The observed mitochondrial changes in COPD epithelium are potentially the consequence of long-term exposure to cigarette smoke, leading to impaired mitochondrial function and may play a role in the pathogenesis of COPD.     

What does biologically meaningful mean? A perspective on gene regulatory network validation

Gene regulatory networks (GRNs) are rapidly being delineated, but their quality and biological meaning are often questioned. Here, I argue that biological meaning is challenging to define and discuss reasons why GRN validation should be interpreted cautiously.     

What's in a name; Genetic structure in Solanum section Petota studied using population-genetic tools

Background  

The taxonomy and systematic relationships among species of Solanum section Petota are complicated and the section seems overclassified. Many of the presumed (sub)species from South America are very similar and they are able to exchange genetic material. We applied a population genetic approach to evaluate support for subgroups within this material, using AFLP data. Our approach is based on the following assumptions: (i) accessions that may exchange genetic material can be analyzed as if they are part of one gene pool, and (ii) genetic differentiation among species is expected to be higher than within species.     

Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models

Background

Artificial neural networks (ANN) mimic the function of the human brain and are capable of performing massively parallel computations for data processing and knowledge representation. ANN can capture nonlinear relationships between predictors and responses and can adaptively learn complex functional forms, in particular, for situations where conventional regression models are ineffective. In a previous study, ANN with Bayesian regularization outperformed a benchmark linear model when predicting milk yield in dairy cattle or grain yield of wheat. Although breeding values rely on the assumption of additive inheritance, the predictive capabilities of ANN are of interest from the perspective of their potential to increase the accuracy of prediction of molecular breeding values used for genomic selection. This motivated the present study, in which the aim was to investigate the accuracy of ANN when predicting the expected progeny difference (EPD) of marbling score in Angus cattle. Various ANN architectures were explored, which involved two training algorithms, two types of activation functions, and from 1 to 4 neurons in hidden layers. For comparison, BayesCπ models were used to select a subset of optimal markers (referred to as feature selection), under the assumption of additive inheritance, and then the marker effects were estimated using BayesCπ with π set equal to zero. This procedure is referred to as BayesCpC and was implemented on a high-throughput computing cluster.

Results

The ANN with Bayesian regularization method performed equally well for prediction of EPD as BayesCpC, based on prediction accuracy and sum of squared errors. With the 3K-SNP panel, for example, prediction accuracy was 0.776 using BayesCpC, and ranged from 0.776 to 0.807 using BRANN. With the selected 700-SNP panel, prediction accuracy was 0.863 for BayesCpC and ranged from 0.842 to 0.858 for BRANN. However, prediction accuracy for the ANN with scaled conjugate gradient back-propagation was lower, ranging from 0.653 to 0.689 with the 3K-SNP panel, and from 0.743 to 0.793 with the selected 700-SNP panel.

Conclusions

ANN with Bayesian regularization performed as well as linear Bayesian regression models in predicting additive genetic values, supporting the idea that ANN are useful as universal approximators of functions of interest in breeding contexts.     

Unraveling Genomic Complexity at a Quantitative Disease Resistance Locus in Maize

Multiple disease resistance has important implications for plant fitness, given the selection pressure that many pathogens exert directly on natural plant populations and indirectly via crop improvement programs. Evidence of a locus conditioning resistance to multiple pathogens was found in bin 1.06 of the maize genome with the allele from inbred line “Tx303” conditioning quantitative resistance to northern leaf blight (NLB) and qualitative resistance to Stewart’s wilt. To dissect the genetic basis of resistance in this region and to refine candidate gene hypotheses, we mapped resistance to the two diseases. Both resistance phenotypes were localized to overlapping regions, with the Stewart’s wilt interval refined to a 95.9-kb segment containing three genes and the NLB interval to a 3.60-Mb segment containing 117 genes. Regions of the introgression showed little to no recombination, suggesting structural differences between the inbred lines Tx303 and “B73,” the parents of the fine-mapping population. We examined copy number variation across the region using next-generation sequencing data, and found large variation in read depth in Tx303 across the region relative to the reference genome of B73. In the fine-mapping region, association mapping for NLB implicated candidate genes, including a putative zinc finger and pan1. We tested mutant alleles and found that pan1 is a susceptibility gene for NLB and Stewart’s wilt. Our data strongly suggest that structural variation plays an important role in resistance conditioned by this region, and pan1, a gene conditioning susceptibility for NLB, may underlie the QTL.     

Selection mapping of loci for quantitative disease resistance in a diverse maize population

The selection response of a complex maize population improved primarily for quantitative disease resistance to northern leaf blight (NLB) and secondarily for common rust resistance and agronomic phenotypes was investigated at the molecular genetic level. A tiered marker analysis with 151 simple sequence repeat (SSR) markers in 90 individuals of the population indicated that on average six alleles per locus were available for selection. An improved test statistic for selection mapping was developed, in which quantitative trait loci (QTL) are identified through the analysis of allele-frequency shifts at mapped multiallelic loci over generations of selection. After correcting for the multiple tests performed, 25 SSR loci showed evidence of selection. Many of the putatively selected loci were unlinked and dispersed across the genome, which was consistent with the diffuse distribution of previously published QTL for NLB resistance. Compelling evidence for selection was found on maize chromosome 8, where several putatively selected loci colocalized with published NLB QTL and a race-specific resistance gene. Analysis of F(2) populations derived from the selection mapping population suggested that multiple linked loci in this chromosomal segment were, in part, responsible for the selection response for quantitative resistance to NLB.